Overview

Dataset statistics

Number of variables22
Number of observations95
Missing cells0
Missing cells (%)0.0%
Duplicate rows0
Duplicate rows (%)0.0%
Total size in memory16.5 KiB
Average record size in memory177.3 B

Variable types

Numeric12
Categorical10

Alerts

city has constant value "Lahore"Constant
province_name has constant value "Punjab"Constant
purpose has constant value "For Sale"Constant
page_url has a high cardinality: 95 distinct valuesHigh cardinality
property_id is highly overall correlated with location_id and 7 other fieldsHigh correlation
location_id is highly overall correlated with property_id and 11 other fieldsHigh correlation
price is highly overall correlated with page_url and 8 other fieldsHigh correlation
latitude is highly overall correlated with location_id and 8 other fieldsHigh correlation
longitude is highly overall correlated with property_id and 13 other fieldsHigh correlation
baths is highly overall correlated with location_id and 12 other fieldsHigh correlation
area_marla is highly overall correlated with page_url and 8 other fieldsHigh correlation
area_sqft is highly overall correlated with page_url and 8 other fieldsHigh correlation
bedrooms is highly overall correlated with page_url and 10 other fieldsHigh correlation
year is highly overall correlated with monthHigh correlation
month is highly overall correlated with location_id and 8 other fieldsHigh correlation
day is highly overall correlated with property_id and 10 other fieldsHigh correlation
page_url is highly overall correlated with property_id and 16 other fieldsHigh correlation
property_type is highly overall correlated with page_url and 4 other fieldsHigh correlation
price_bin is highly overall correlated with location_id and 8 other fieldsHigh correlation
location is highly overall correlated with property_id and 16 other fieldsHigh correlation
locality is highly overall correlated with property_id and 16 other fieldsHigh correlation
area is highly overall correlated with property_id and 16 other fieldsHigh correlation
date_added is highly overall correlated with property_id and 15 other fieldsHigh correlation
page_url is uniformly distributedUniform
property_id has unique valuesUnique
page_url has unique valuesUnique
baths has 16 (16.8%) zerosZeros
bedrooms has 12 (12.6%) zerosZeros

Reproduction

Analysis started2022-12-04 22:47:28.770237
Analysis finished2022-12-04 22:47:47.852909
Duration19.08 seconds
Software versionpandas-profiling vv3.5.0
Download configurationconfig.json

Variables

property_id
Real number (ℝ)

HIGH CORRELATION
UNIQUE

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3371864.1
Minimum347795
Maximum5019310
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:47.901747image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum347795
5-th percentile983065.7
Q12471475
median3931998
Q34323660
95-th percentile4698194.6
Maximum5019310
Range4671515
Interquartile range (IQR)1852185

Descriptive statistics

Standard deviation1176444.8
Coefficient of variation (CV)0.34890042
Kurtosis-0.26253121
Mean3371864.1
Median Absolute Deviation (MAD)625801
Skewness-0.8067837
Sum3.2032709 × 108
Variance1.3840224 × 1012
MonotonicityStrictly increasing
2022-12-05T03:47:47.984470image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
347795 1
 
1.1%
4017113 1
 
1.1%
4304977 1
 
1.1%
4249571 1
 
1.1%
4170552 1
 
1.1%
4159826 1
 
1.1%
4153988 1
 
1.1%
4111694 1
 
1.1%
4107715 1
 
1.1%
4100382 1
 
1.1%
Other values (85) 85
89.5%
ValueCountFrequency (%)
347795 1
1.1%
482892 1
1.1%
555962 1
1.1%
785289 1
1.1%
983065 1
1.1%
983066 1
1.1%
1286643 1
1.1%
1402784 1
1.1%
1606710 1
1.1%
1646880 1
1.1%
ValueCountFrequency (%)
5019310 1
1.1%
4951451 1
1.1%
4940629 1
1.1%
4907568 1
1.1%
4747413 1
1.1%
4677101 1
1.1%
4570988 1
1.1%
4560911 1
1.1%
4557799 1
1.1%
4536854 1
1.1%

location_id
Real number (ℝ)

Distinct55
Distinct (%)57.9%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5125.2947
Minimum7
Maximum10542
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:48.147575image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum7
5-th percentile73.2
Q11604
median3847
Q39433
95-th percentile9436
Maximum10542
Range10535
Interquartile range (IQR)7829

Descriptive statistics

Standard deviation3727.3855
Coefficient of variation (CV)0.72725291
Kurtosis-1.6461484
Mean5125.2947
Median Absolute Deviation (MAD)3474
Skewness0.099539953
Sum486903
Variance13893403
MonotonicityNot monotonic
2022-12-05T03:47:48.228341image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
9435 9
 
9.5%
9434 6
 
6.3%
9433 6
 
6.3%
8172 5
 
5.3%
9436 4
 
4.2%
1784 3
 
3.2%
10542 3
 
3.2%
514 3
 
3.2%
9432 3
 
3.2%
3824 2
 
2.1%
Other values (45) 51
53.7%
ValueCountFrequency (%)
7 1
1.1%
8 2
2.1%
48 1
1.1%
69 1
1.1%
75 1
1.1%
154 2
2.1%
373 1
1.1%
377 1
1.1%
378 1
1.1%
496 1
1.1%
ValueCountFrequency (%)
10542 3
 
3.2%
9747 1
 
1.1%
9436 4
4.2%
9435 9
9.5%
9434 6
6.3%
9433 6
6.3%
9432 3
 
3.2%
8444 1
 
1.1%
8425 1
 
1.1%
8172 5
5.3%

page_url
Categorical

HIGH CARDINALITY
HIGH CORRELATION
UNIFORM
UNIQUE

Distinct95
Distinct (%)100.0%
Missing0
Missing (%)0.0%
Memory size888.0 B
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
 
1
https://www.zameen.com/Property/dha_defence_defence_raya_1_kanal_house_for_sale_at_facing_golf_park_dha_raya-4017113-8172-1.html
 
1
https://www.zameen.com/Property/dha_defence_dha_phase_4_house_for_sale_in_dha_phase_4-4304977-1446-1.html
 
1
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html
 
1
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html
 
1
Other values (90)
90 

Length

Max length165
Median length135
Mean length124.85263
Min length88

Characters and Unicode

Total characters11861
Distinct characters42
Distinct categories6 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique95 ?
Unique (%)100.0%

Sample

1st rowhttps://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html
2nd rowhttps://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.html
3rd rowhttps://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html
4th rowhttps://www.zameen.com/Property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.html
5th rowhttps://www.zameen.com/Property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.html

Common Values

ValueCountFrequency (%)
https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html 1
 
1.1%
https://www.zameen.com/Property/dha_defence_defence_raya_1_kanal_house_for_sale_at_facing_golf_park_dha_raya-4017113-8172-1.html 1
 
1.1%
https://www.zameen.com/Property/dha_defence_dha_phase_4_house_for_sale_in_dha_phase_4-4304977-1446-1.html 1
 
1.1%
https://www.zameen.com/Property/gulberg_gulberg_4_exclusive_location_house_for_sale-4249571-3847-1.html 1
 
1.1%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_double_storey_house_for_sale-4170552-3527-1.html 1
 
1.1%
https://www.zameen.com/Property/allama_iqbal_town_allama_iqbal_town_pak_block_2_marla_house_is_available_for_sale-4159826-3527-1.html 1
 
1.1%
https://www.zameen.com/Property/bahria_town_sector_c_bahria_town_tulip_block_10_marla_brand_new_house_in_sector_c_bahria_town_lahore-4153988-1789-1.html 1
 
1.1%
https://www.zameen.com/Property/dha_phase_5_dha_phase_5_block_a_1_kanal_beautiful_bungalow_house_for_sale-4111694-1598-1.html 1
 
1.1%
https://www.zameen.com/Property/dha_defence_dha_phase_6_1_kanal_brand_new_beautiful_bungalow_for_sale-4107715-1448-1.html 1
 
1.1%
https://www.zameen.com/Property/askari_10_askari_10_sector_f_17_marla_corner_brand_new_brig_house_available_for_sale-4100382-10542-1.html 1
 
1.1%
Other values (85) 85
89.5%

Length

2022-12-05T03:47:48.325978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
https://www.zameen.com/property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.html 1
 
1.1%
https://www.zameen.com/property/askari_10_askari_10_sector_b_one_kanal_house_khalid_designed_main_boulevard_fully_renovated_available_for_sale-2471472-9433-1.html 1
 
1.1%
https://www.zameen.com/property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.html 1
 
1.1%
https://www.zameen.com/property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.html 1
 
1.1%
https://www.zameen.com/property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.html 1
 
1.1%
https://www.zameen.com/property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.html 1
 
1.1%
https://www.zameen.com/property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.html 1
 
1.1%
https://www.zameen.com/property/lahore_upper_mall_commercial_old_house_for_sale_upper_mall_lahore_excellent_location-1402784-514-1.html 1
 
1.1%
https://www.zameen.com/property/lahore_cavalry_ground_10_marla_house_is_available_for_sale-1606710-69-1.html 1
 
1.1%
https://www.zameen.com/property/bahria_town_sector_b_bahria_town_umar_block_double_storey_house_for_sale-1646880-1781-1.html 1
 
1.1%
Other values (85) 85
89.5%

Most occurring characters

ValueCountFrequency (%)
_ 1181
 
10.0%
a 917
 
7.7%
e 904
 
7.6%
r 671
 
5.7%
o 605
 
5.1%
t 557
 
4.7%
l 531
 
4.5%
s 475
 
4.0%
m 419
 
3.5%
/ 380
 
3.2%
Other values (32) 5221
44.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 8173
68.9%
Decimal Number 1367
 
11.5%
Connector Punctuation 1181
 
10.0%
Other Punctuation 760
 
6.4%
Dash Punctuation 285
 
2.4%
Uppercase Letter 95
 
0.8%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 917
 
11.2%
e 904
 
11.1%
r 671
 
8.2%
o 605
 
7.4%
t 557
 
6.8%
l 531
 
6.5%
s 475
 
5.8%
m 419
 
5.1%
h 376
 
4.6%
n 349
 
4.3%
Other values (16) 2369
29.0%
Decimal Number
ValueCountFrequency (%)
1 320
23.4%
4 178
13.0%
3 150
11.0%
0 145
10.6%
9 118
 
8.6%
5 105
 
7.7%
2 104
 
7.6%
7 101
 
7.4%
8 86
 
6.3%
6 60
 
4.4%
Other Punctuation
ValueCountFrequency (%)
/ 380
50.0%
. 285
37.5%
: 95
 
12.5%
Connector Punctuation
ValueCountFrequency (%)
_ 1181
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 285
100.0%
Uppercase Letter
ValueCountFrequency (%)
P 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 8268
69.7%
Common 3593
30.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 917
 
11.1%
e 904
 
10.9%
r 671
 
8.1%
o 605
 
7.3%
t 557
 
6.7%
l 531
 
6.4%
s 475
 
5.7%
m 419
 
5.1%
h 376
 
4.5%
n 349
 
4.2%
Other values (17) 2464
29.8%
Common
ValueCountFrequency (%)
_ 1181
32.9%
/ 380
 
10.6%
1 320
 
8.9%
. 285
 
7.9%
- 285
 
7.9%
4 178
 
5.0%
3 150
 
4.2%
0 145
 
4.0%
9 118
 
3.3%
5 105
 
2.9%
Other values (5) 446
 
12.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 11861
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
_ 1181
 
10.0%
a 917
 
7.7%
e 904
 
7.6%
r 671
 
5.7%
o 605
 
5.1%
t 557
 
4.7%
l 531
 
4.5%
s 475
 
4.0%
m 419
 
3.5%
/ 380
 
3.2%
Other values (32) 5221
44.0%

property_type
Categorical

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
House
93 
Flat
 
2

Length

Max length5
Median length5
Mean length4.9789474
Min length4

Characters and Unicode

Total characters473
Distinct characters9
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowHouse
2nd rowHouse
3rd rowHouse
4th rowHouse
5th rowHouse

Common Values

ValueCountFrequency (%)
House 93
97.9%
Flat 2
 
2.1%

Length

2022-12-05T03:47:48.403761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-05T03:47:48.473485image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
house 93
97.9%
flat 2
 
2.1%

Most occurring characters

ValueCountFrequency (%)
H 93
19.7%
o 93
19.7%
u 93
19.7%
s 93
19.7%
e 93
19.7%
F 2
 
0.4%
l 2
 
0.4%
a 2
 
0.4%
t 2
 
0.4%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 378
79.9%
Uppercase Letter 95
 
20.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 93
24.6%
u 93
24.6%
s 93
24.6%
e 93
24.6%
l 2
 
0.5%
a 2
 
0.5%
t 2
 
0.5%
Uppercase Letter
ValueCountFrequency (%)
H 93
97.9%
F 2
 
2.1%

Most occurring scripts

ValueCountFrequency (%)
Latin 473
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
H 93
19.7%
o 93
19.7%
u 93
19.7%
s 93
19.7%
e 93
19.7%
F 2
 
0.4%
l 2
 
0.4%
a 2
 
0.4%
t 2
 
0.4%

Most occurring blocks

ValueCountFrequency (%)
ASCII 473
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
H 93
19.7%
o 93
19.7%
u 93
19.7%
s 93
19.7%
e 93
19.7%
F 2
 
0.4%
l 2
 
0.4%
a 2
 
0.4%
t 2
 
0.4%

price
Real number (ℝ)

Distinct64
Distinct (%)67.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean78244211
Minimum3200000
Maximum1.25 × 109
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:48.538269image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum3200000
5-th percentile7190000
Q120750000
median23500000
Q349750000
95-th percentile3.66 × 108
Maximum1.25 × 109
Range1.2468 × 109
Interquartile range (IQR)29000000

Descriptive statistics

Standard deviation1.9351258 × 108
Coefficient of variation (CV)2.4731872
Kurtosis25.986397
Mean78244211
Median Absolute Deviation (MAD)11500000
Skewness4.9270411
Sum7.4332 × 109
Variance3.744712 × 1016
MonotonicityNot monotonic
2022-12-05T03:47:48.623982image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
22500000 5
 
5.3%
23000000 5
 
5.3%
40000000 4
 
4.2%
23500000 4
 
4.2%
21500000 4
 
4.2%
35000000 3
 
3.2%
50000000 3
 
3.2%
22000000 3
 
3.2%
21000000 2
 
2.1%
3200000 2
 
2.1%
Other values (54) 60
63.2%
ValueCountFrequency (%)
3200000 2
2.1%
5500000 2
2.1%
6000000 1
1.1%
7700000 1
1.1%
8000000 1
1.1%
9500000 1
1.1%
11800000 1
1.1%
12000000 2
2.1%
12500000 1
1.1%
13500000 1
1.1%
ValueCountFrequency (%)
1250000000 1
1.1%
1200000000 1
1.1%
620000000 1
1.1%
480000000 1
1.1%
380000000 1
1.1%
360000000 1
1.1%
220000000 1
1.1%
180000000 1
1.1%
160000000 1
1.1%
87500000 1
1.1%

price_bin
Categorical

Distinct4
Distinct (%)4.2%
Missing0
Missing (%)0.0%
Memory size888.0 B
Very High
41 
High
40 
Low
Medium

Length

Max length9
Median length6
Mean length6.2
Min length3

Characters and Unicode

Total characters589
Distinct characters16
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowVery High
2nd rowVery High
3rd rowLow
4th rowVery High
5th rowHigh

Common Values

ValueCountFrequency (%)
Very High 41
43.2%
High 40
42.1%
Low 8
 
8.4%
Medium 6
 
6.3%

Length

2022-12-05T03:47:48.699730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-05T03:47:48.769495image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
high 81
59.6%
very 41
30.1%
low 8
 
5.9%
medium 6
 
4.4%

Most occurring characters

ValueCountFrequency (%)
i 87
14.8%
H 81
13.8%
g 81
13.8%
h 81
13.8%
e 47
8.0%
V 41
7.0%
r 41
7.0%
y 41
7.0%
41
7.0%
L 8
 
1.4%
Other values (6) 40
6.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 412
69.9%
Uppercase Letter 136
 
23.1%
Space Separator 41
 
7.0%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
i 87
21.1%
g 81
19.7%
h 81
19.7%
e 47
11.4%
r 41
10.0%
y 41
10.0%
o 8
 
1.9%
w 8
 
1.9%
d 6
 
1.5%
u 6
 
1.5%
Uppercase Letter
ValueCountFrequency (%)
H 81
59.6%
V 41
30.1%
L 8
 
5.9%
M 6
 
4.4%
Space Separator
ValueCountFrequency (%)
41
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 548
93.0%
Common 41
 
7.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
i 87
15.9%
H 81
14.8%
g 81
14.8%
h 81
14.8%
e 47
8.6%
V 41
7.5%
r 41
7.5%
y 41
7.5%
L 8
 
1.5%
o 8
 
1.5%
Other values (5) 32
 
5.8%
Common
ValueCountFrequency (%)
41
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 589
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
i 87
14.8%
H 81
13.8%
g 81
13.8%
h 81
13.8%
e 47
8.0%
V 41
7.0%
r 41
7.0%
y 41
7.0%
41
7.0%
L 8
 
1.4%
Other values (6) 40
6.8%

location
Categorical

Distinct28
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
Askari
31 
Gulberg
12 
DHA Defence
10 
Bahria Town
EME Society
Other values (23)
31 

Length

Max length36
Median length24
Mean length9.4526316
Min length3

Characters and Unicode

Total characters898
Distinct characters43
Distinct categories4 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)17.9%

Sample

1st rowModel Town
2nd rowMultan Road
3rd rowEden
4th rowGulberg
5th rowEME Society

Common Values

ValueCountFrequency (%)
Askari 31
32.6%
Gulberg 12
 
12.6%
DHA Defence 10
 
10.5%
Bahria Town 6
 
6.3%
EME Society 5
 
5.3%
Allama Iqbal Town 3
 
3.2%
Upper Mall 3
 
3.2%
Paragon City 2
 
2.1%
Model Town 2
 
2.1%
Eden 2
 
2.1%
Other values (18) 19
20.0%

Length

2022-12-05T03:47:48.834278image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
askari 31
20.8%
town 14
 
9.4%
gulberg 12
 
8.1%
dha 10
 
6.7%
defence 10
 
6.7%
society 8
 
5.4%
bahria 6
 
4.0%
eme 5
 
3.4%
housing 3
 
2.0%
allama 3
 
2.0%
Other values (32) 47
31.5%

Most occurring characters

ValueCountFrequency (%)
a 88
 
9.8%
e 74
 
8.2%
r 66
 
7.3%
i 60
 
6.7%
54
 
6.0%
n 50
 
5.6%
A 49
 
5.5%
s 39
 
4.3%
l 35
 
3.9%
o 33
 
3.7%
Other values (33) 350
39.0%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 656
73.1%
Uppercase Letter 185
 
20.6%
Space Separator 54
 
6.0%
Dash Punctuation 3
 
0.3%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 88
13.4%
e 74
11.3%
r 66
10.1%
i 60
 
9.1%
n 50
 
7.6%
s 39
 
5.9%
l 35
 
5.3%
o 33
 
5.0%
k 33
 
5.0%
c 23
 
3.5%
Other values (14) 155
23.6%
Uppercase Letter
ValueCountFrequency (%)
A 49
26.5%
D 20
10.8%
G 17
 
9.2%
E 15
 
8.1%
T 14
 
7.6%
M 13
 
7.0%
H 13
 
7.0%
S 10
 
5.4%
B 7
 
3.8%
C 6
 
3.2%
Other values (7) 21
11.4%
Space Separator
ValueCountFrequency (%)
54
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 841
93.7%
Common 57
 
6.3%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 88
 
10.5%
e 74
 
8.8%
r 66
 
7.8%
i 60
 
7.1%
n 50
 
5.9%
A 49
 
5.8%
s 39
 
4.6%
l 35
 
4.2%
o 33
 
3.9%
k 33
 
3.9%
Other values (31) 314
37.3%
Common
ValueCountFrequency (%)
54
94.7%
- 3
 
5.3%

Most occurring blocks

ValueCountFrequency (%)
ASCII 898
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 88
 
9.8%
e 74
 
8.2%
r 66
 
7.3%
i 60
 
6.7%
54
 
6.0%
n 50
 
5.6%
A 49
 
5.5%
s 39
 
4.3%
l 35
 
3.9%
o 33
 
3.7%
Other values (33) 350
39.0%

city
Categorical

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
Lahore
95 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters570
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowLahore
2nd rowLahore
3rd rowLahore
4th rowLahore
5th rowLahore

Common Values

ValueCountFrequency (%)
Lahore 95
100.0%

Length

2022-12-05T03:47:48.898067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-05T03:47:48.957865image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
lahore 95
100.0%

Most occurring characters

ValueCountFrequency (%)
L 95
16.7%
a 95
16.7%
h 95
16.7%
o 95
16.7%
r 95
16.7%
e 95
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 475
83.3%
Uppercase Letter 95
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 95
20.0%
h 95
20.0%
o 95
20.0%
r 95
20.0%
e 95
20.0%
Uppercase Letter
ValueCountFrequency (%)
L 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
L 95
16.7%
a 95
16.7%
h 95
16.7%
o 95
16.7%
r 95
16.7%
e 95
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
L 95
16.7%
a 95
16.7%
h 95
16.7%
o 95
16.7%
r 95
16.7%
e 95
16.7%

province_name
Categorical

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
Punjab
95 

Length

Max length6
Median length6
Mean length6
Min length6

Characters and Unicode

Total characters570
Distinct characters6
Distinct categories2 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowPunjab
2nd rowPunjab
3rd rowPunjab
4th rowPunjab
5th rowPunjab

Common Values

ValueCountFrequency (%)
Punjab 95
100.0%

Length

2022-12-05T03:47:49.005462image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-05T03:47:49.060281image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
punjab 95
100.0%

Most occurring characters

ValueCountFrequency (%)
P 95
16.7%
u 95
16.7%
n 95
16.7%
j 95
16.7%
a 95
16.7%
b 95
16.7%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 475
83.3%
Uppercase Letter 95
 
16.7%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
u 95
20.0%
n 95
20.0%
j 95
20.0%
a 95
20.0%
b 95
20.0%
Uppercase Letter
ValueCountFrequency (%)
P 95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 570
100.0%

Most frequent character per script

Latin
ValueCountFrequency (%)
P 95
16.7%
u 95
16.7%
n 95
16.7%
j 95
16.7%
a 95
16.7%
b 95
16.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 570
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
P 95
16.7%
u 95
16.7%
n 95
16.7%
j 95
16.7%
a 95
16.7%
b 95
16.7%

locality
Categorical

Distinct28
Distinct (%)29.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
Askari, Lahore, Punjab
31 
Gulberg, Lahore, Punjab
12 
DHA Defence, Lahore, Punjab
10 
Bahria Town, Lahore, Punjab
EME Society, Lahore, Punjab
Other values (23)
31 

Length

Max length52
Median length40
Mean length25.452632
Min length19

Characters and Unicode

Total characters2418
Distinct characters46
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique17 ?
Unique (%)17.9%

Sample

1st rowModel Town, Lahore, Punjab
2nd rowMultan Road, Lahore, Punjab
3rd rowEden, Lahore, Punjab
4th rowGulberg, Lahore, Punjab
5th rowEME Society, Lahore, Punjab

Common Values

ValueCountFrequency (%)
Askari, Lahore, Punjab 31
32.6%
Gulberg, Lahore, Punjab 12
 
12.6%
DHA Defence, Lahore, Punjab 10
 
10.5%
Bahria Town, Lahore, Punjab 6
 
6.3%
EME Society, Lahore, Punjab 5
 
5.3%
Allama Iqbal Town, Lahore, Punjab 3
 
3.2%
Upper Mall, Lahore, Punjab 3
 
3.2%
Paragon City, Lahore, Punjab 2
 
2.1%
Model Town, Lahore, Punjab 2
 
2.1%
Eden, Lahore, Punjab 2
 
2.1%
Other values (18) 19
20.0%

Length

2022-12-05T03:47:49.113105image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
punjab 95
28.0%
lahore 95
28.0%
askari 31
 
9.1%
town 14
 
4.1%
gulberg 12
 
3.5%
dha 10
 
2.9%
defence 10
 
2.9%
society 8
 
2.4%
bahria 6
 
1.8%
eme 5
 
1.5%
Other values (34) 53
15.6%

Most occurring characters

ValueCountFrequency (%)
a 278
 
11.5%
244
 
10.1%
, 190
 
7.9%
e 169
 
7.0%
r 161
 
6.7%
n 145
 
6.0%
o 128
 
5.3%
u 116
 
4.8%
b 111
 
4.6%
h 107
 
4.4%
Other values (36) 769
31.8%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 1606
66.4%
Uppercase Letter 375
 
15.5%
Space Separator 244
 
10.1%
Other Punctuation 190
 
7.9%
Dash Punctuation 3
 
0.1%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
a 278
17.3%
e 169
10.5%
r 161
10.0%
n 145
9.0%
o 128
8.0%
u 116
7.2%
b 111
 
6.9%
h 107
 
6.7%
j 95
 
5.9%
i 60
 
3.7%
Other values (15) 236
14.7%
Uppercase Letter
ValueCountFrequency (%)
P 100
26.7%
L 95
25.3%
A 49
13.1%
D 20
 
5.3%
G 17
 
4.5%
E 15
 
4.0%
T 14
 
3.7%
H 13
 
3.5%
M 13
 
3.5%
S 10
 
2.7%
Other values (8) 29
 
7.7%
Space Separator
ValueCountFrequency (%)
244
100.0%
Other Punctuation
ValueCountFrequency (%)
, 190
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 3
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 1981
81.9%
Common 437
 
18.1%

Most frequent character per script

Latin
ValueCountFrequency (%)
a 278
14.0%
e 169
 
8.5%
r 161
 
8.1%
n 145
 
7.3%
o 128
 
6.5%
u 116
 
5.9%
b 111
 
5.6%
h 107
 
5.4%
P 100
 
5.0%
L 95
 
4.8%
Other values (33) 571
28.8%
Common
ValueCountFrequency (%)
244
55.8%
, 190
43.5%
- 3
 
0.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 2418
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 278
 
11.5%
244
 
10.1%
, 190
 
7.9%
e 169
 
7.0%
r 161
 
6.7%
n 145
 
6.0%
o 128
 
5.3%
u 116
 
4.8%
b 111
 
4.6%
h 107
 
4.4%
Other values (36) 769
31.8%

latitude
Real number (ℝ)

Distinct57
Distinct (%)60.0%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean31.501448
Minimum31.37108
Maximum31.717279
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:49.192838image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum31.37108
5-th percentile31.379518
Q131.467691
median31.522361
Q331.536068
95-th percentile31.556202
Maximum31.717279
Range0.34619872
Interquartile range (IQR)0.068377

Descriptive statistics

Standard deviation0.057218636
Coefficient of variation (CV)0.0018163812
Kurtosis1.5960675
Mean31.501448
Median Absolute Deviation (MAD)0.019753
Skewness-0.22426899
Sum2992.6375
Variance0.0032739724
MonotonicityNot monotonic
2022-12-05T03:47:49.270577image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
31.533161 8
 
8.4%
31.537458 6
 
6.3%
31.469155 5
 
5.3%
31.53327 5
 
5.3%
31.536068 4
 
4.2%
31.537239 3
 
3.2%
31.374195 3
 
3.2%
31.533849 3
 
3.2%
31.542114 3
 
3.2%
31.518337 2
 
2.1%
Other values (47) 53
55.8%
ValueCountFrequency (%)
31.37108 1
 
1.1%
31.374195 3
3.2%
31.374414 1
 
1.1%
31.381706 1
 
1.1%
31.400096 1
 
1.1%
31.402513 1
 
1.1%
31.40537 1
 
1.1%
31.431593 1
 
1.1%
31.434668 1
 
1.1%
31.437744 2
2.1%
ValueCountFrequency (%)
31.71727872 1
 
1.1%
31.597234 1
 
1.1%
31.590234 1
 
1.1%
31.57443055 1
 
1.1%
31.567912 1
 
1.1%
31.551184 1
 
1.1%
31.54913521 1
 
1.1%
31.543431 1
 
1.1%
31.542114 3
3.2%
31.539206 1
 
1.1%

longitude
Real number (ℝ)

Distinct58
Distinct (%)61.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean74.357936
Minimum74.177749
Maximum74.474513
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:49.352808image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum74.177749
5-th percentile74.191482
Q174.30294
median74.385309
Q374.419481
95-th percentile74.470553
Maximum74.474513
Range0.296764
Interquartile range (IQR)0.116541

Descriptive statistics

Standard deviation0.083130273
Coefficient of variation (CV)0.0011179745
Kurtosis-0.52166294
Mean74.357936
Median Absolute Deviation (MAD)0.039761
Skewness-0.73803026
Sum7064.0039
Variance0.0069106423
MonotonicityNot monotonic
2022-12-05T03:47:49.431545image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=50)
ValueCountFrequency (%)
74.419481 8
 
8.4%
74.413323 6
 
6.3%
74.470553 5
 
5.3%
74.41358 5
 
5.3%
74.408666 4
 
4.2%
74.420211 3
 
3.2%
74.191482 3
 
3.2%
74.409633 3
 
3.2%
74.355898 3
 
3.2%
74.348202 2
 
2.1%
Other values (48) 53
55.8%
ValueCountFrequency (%)
74.177749 1
 
1.1%
74.17998 1
 
1.1%
74.190316 1
 
1.1%
74.191482 3
3.2%
74.195294 1
 
1.1%
74.209685 2
2.1%
74.21349 2
2.1%
74.214005 1
 
1.1%
74.224648 1
 
1.1%
74.239683 1
 
1.1%
ValueCountFrequency (%)
74.474513 1
 
1.1%
74.470553 5
5.3%
74.456184 1
 
1.1%
74.45599191 1
 
1.1%
74.451342 1
 
1.1%
74.445906 1
 
1.1%
74.44369633 1
 
1.1%
74.440012 1
 
1.1%
74.429569 1
 
1.1%
74.427137 1
 
1.1%

baths
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct8
Distinct (%)8.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean4.3263158
Minimum0
Maximum8
Zeros16
Zeros (%)16.8%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:49.501315image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13.5
median5
Q36
95-th percentile7
Maximum8
Range8
Interquartile range (IQR)2.5

Descriptive statistics

Standard deviation2.3083664
Coefficient of variation (CV)0.53356401
Kurtosis-0.35517021
Mean4.3263158
Median Absolute Deviation (MAD)1
Skewness-0.83280733
Sum411
Variance5.3285554
MonotonicityNot monotonic
2022-12-05T03:47:49.556497image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=8)
ValueCountFrequency (%)
5 27
28.4%
6 22
23.2%
0 16
16.8%
4 11
11.6%
7 8
 
8.4%
3 4
 
4.2%
2 4
 
4.2%
8 3
 
3.2%
ValueCountFrequency (%)
0 16
16.8%
2 4
 
4.2%
3 4
 
4.2%
4 11
11.6%
5 27
28.4%
6 22
23.2%
7 8
 
8.4%
8 3
 
3.2%
ValueCountFrequency (%)
8 3
 
3.2%
7 8
 
8.4%
6 22
23.2%
5 27
28.4%
4 11
11.6%
3 4
 
4.2%
2 4
 
4.2%
0 16
16.8%

area
Categorical

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Memory size888.0 B
10 Marla
33 
1 Kanal
21 
2 Kanal
3 Marla
 
3
12 Marla
 
3
Other values (24)
30 

Length

Max length9
Median length8
Mean length7.6736842
Min length7

Characters and Unicode

Total characters729
Distinct characters18
Distinct categories5 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique19 ?
Unique (%)20.0%

Sample

1st row6 Kanal
2nd row1 Kanal
3rd row9 Marla
4th row1 Kanal
5th row1 Kanal

Common Values

ValueCountFrequency (%)
10 Marla 33
34.7%
1 Kanal 21
22.1%
2 Kanal 5
 
5.3%
3 Marla 3
 
3.2%
12 Marla 3
 
3.2%
17 Marla 3
 
3.2%
18 Marla 2
 
2.1%
2 Marla 2
 
2.1%
1.1 Kanal 2
 
2.1%
8 Kanal 2
 
2.1%
Other values (19) 19
20.0%

Length

2022-12-05T03:47:49.627904image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
marla 59
31.1%
kanal 36
18.9%
10 34
17.9%
1 21
 
11.1%
2 7
 
3.7%
3 3
 
1.6%
12 3
 
1.6%
17 3
 
1.6%
8 3
 
1.6%
6 2
 
1.1%
Other values (16) 19
 
10.0%

Most occurring characters

ValueCountFrequency (%)
a 190
26.1%
95
13.0%
l 95
13.0%
1 71
 
9.7%
M 59
 
8.1%
r 59
 
8.1%
K 36
 
4.9%
n 36
 
4.9%
0 34
 
4.7%
2 13
 
1.8%
Other values (8) 41
 
5.6%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 380
52.1%
Decimal Number 149
 
20.4%
Space Separator 95
 
13.0%
Uppercase Letter 95
 
13.0%
Other Punctuation 10
 
1.4%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
1 71
47.7%
0 34
22.8%
2 13
 
8.7%
7 7
 
4.7%
8 6
 
4.0%
5 6
 
4.0%
4 4
 
2.7%
3 3
 
2.0%
6 3
 
2.0%
9 2
 
1.3%
Lowercase Letter
ValueCountFrequency (%)
a 190
50.0%
l 95
25.0%
r 59
 
15.5%
n 36
 
9.5%
Uppercase Letter
ValueCountFrequency (%)
M 59
62.1%
K 36
37.9%
Space Separator
ValueCountFrequency (%)
95
100.0%
Other Punctuation
ValueCountFrequency (%)
. 10
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 475
65.2%
Common 254
34.8%

Most frequent character per script

Common
ValueCountFrequency (%)
95
37.4%
1 71
28.0%
0 34
 
13.4%
2 13
 
5.1%
. 10
 
3.9%
7 7
 
2.8%
8 6
 
2.4%
5 6
 
2.4%
4 4
 
1.6%
3 3
 
1.2%
Other values (2) 5
 
2.0%
Latin
ValueCountFrequency (%)
a 190
40.0%
l 95
20.0%
M 59
 
12.4%
r 59
 
12.4%
K 36
 
7.6%
n 36
 
7.6%

Most occurring blocks

ValueCountFrequency (%)
ASCII 729
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
a 190
26.1%
95
13.0%
l 95
13.0%
1 71
 
9.7%
M 59
 
8.1%
r 59
 
8.1%
K 36
 
4.9%
n 36
 
4.9%
0 34
 
4.7%
2 13
 
1.8%
Other values (8) 41
 
5.6%

area_marla
Real number (ℝ)

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean23.109474
Minimum2
Maximum200
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:49.699665image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2
5-th percentile3
Q110
median10
Q320
95-th percentile101.8
Maximum200
Range198
Interquartile range (IQR)10

Descriptive statistics

Standard deviation34.374276
Coefficient of variation (CV)1.4874539
Kurtosis12.650675
Mean23.109474
Median Absolute Deviation (MAD)6
Skewness3.5271151
Sum2195.4
Variance1181.5909
MonotonicityNot monotonic
2022-12-05T03:47:49.848167image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
10 33
34.7%
20 21
22.1%
40 5
 
5.3%
3 3
 
3.2%
12 3
 
3.2%
17 3
 
3.2%
18 2
 
2.1%
2 2
 
2.1%
22 2
 
2.1%
160 2
 
2.1%
Other values (19) 19
20.0%
ValueCountFrequency (%)
2 2
2.1%
2.5 1
 
1.1%
3 3
3.2%
4 1
 
1.1%
5 1
 
1.1%
5.5 1
 
1.1%
6 1
 
1.1%
7 1
 
1.1%
7.5 1
 
1.1%
8 1
 
1.1%
ValueCountFrequency (%)
200 1
 
1.1%
160 2
 
2.1%
130 1
 
1.1%
120 1
 
1.1%
94 1
 
1.1%
80 1
 
1.1%
40 5
 
5.3%
24 1
 
1.1%
22 2
 
2.1%
20 21
22.1%

area_sqft
Real number (ℝ)

Distinct29
Distinct (%)30.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean6291.5771
Minimum544.5
Maximum54450.2
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:49.921921image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum544.5
5-th percentile816.75
Q12722.51
median2722.51
Q35445.02
95-th percentile27715.149
Maximum54450.2
Range53905.7
Interquartile range (IQR)2722.51

Descriptive statistics

Standard deviation9358.4311
Coefficient of variation (CV)1.4874539
Kurtosis12.650675
Mean6291.5771
Median Absolute Deviation (MAD)1633.51
Skewness3.5271151
Sum597699.82
Variance87580233
MonotonicityNot monotonic
2022-12-05T03:47:49.995187image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=29)
ValueCountFrequency (%)
2722.51 33
34.7%
5445.02 21
22.1%
10890.04 5
 
5.3%
816.75 3
 
3.2%
3267.01 3
 
3.2%
4628.27 3
 
3.2%
4900.52 2
 
2.1%
544.5 2
 
2.1%
5989.52 2
 
2.1%
43560.16 2
 
2.1%
Other values (19) 19
20.0%
ValueCountFrequency (%)
544.5 2
2.1%
680.63 1
 
1.1%
816.75 3
3.2%
1089 1
 
1.1%
1361.25 1
 
1.1%
1497.38 1
 
1.1%
1633.51 1
 
1.1%
1905.76 1
 
1.1%
2041.88 1
 
1.1%
2178.01 1
 
1.1%
ValueCountFrequency (%)
54450.2 1
 
1.1%
43560.16 2
 
2.1%
35392.63 1
 
1.1%
32670.12 1
 
1.1%
25591.59 1
 
1.1%
21780.08 1
 
1.1%
10890.04 5
 
5.3%
6534.02 1
 
1.1%
5989.52 2
 
2.1%
5445.02 21
22.1%

purpose
Categorical

Distinct1
Distinct (%)1.1%
Missing0
Missing (%)0.0%
Memory size888.0 B
For Sale
95 

Length

Max length8
Median length8
Mean length8
Min length8

Characters and Unicode

Total characters760
Distinct characters8
Distinct categories3 ?
Distinct scripts2 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique0 ?
Unique (%)0.0%

Sample

1st rowFor Sale
2nd rowFor Sale
3rd rowFor Sale
4th rowFor Sale
5th rowFor Sale

Common Values

ValueCountFrequency (%)
For Sale 95
100.0%

Length

2022-12-05T03:47:50.063957image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category

Common Values (Plot)

2022-12-05T03:47:50.121764image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
ValueCountFrequency (%)
for 95
50.0%
sale 95
50.0%

Most occurring characters

ValueCountFrequency (%)
F 95
12.5%
o 95
12.5%
r 95
12.5%
95
12.5%
S 95
12.5%
a 95
12.5%
l 95
12.5%
e 95
12.5%

Most occurring categories

ValueCountFrequency (%)
Lowercase Letter 475
62.5%
Uppercase Letter 190
 
25.0%
Space Separator 95
 
12.5%

Most frequent character per category

Lowercase Letter
ValueCountFrequency (%)
o 95
20.0%
r 95
20.0%
a 95
20.0%
l 95
20.0%
e 95
20.0%
Uppercase Letter
ValueCountFrequency (%)
F 95
50.0%
S 95
50.0%
Space Separator
ValueCountFrequency (%)
95
100.0%

Most occurring scripts

ValueCountFrequency (%)
Latin 665
87.5%
Common 95
 
12.5%

Most frequent character per script

Latin
ValueCountFrequency (%)
F 95
14.3%
o 95
14.3%
r 95
14.3%
S 95
14.3%
a 95
14.3%
l 95
14.3%
e 95
14.3%
Common
ValueCountFrequency (%)
95
100.0%

Most occurring blocks

ValueCountFrequency (%)
ASCII 760
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
F 95
12.5%
o 95
12.5%
r 95
12.5%
95
12.5%
S 95
12.5%
a 95
12.5%
l 95
12.5%
e 95
12.5%

bedrooms
Real number (ℝ)

HIGH CORRELATION
ZEROS

Distinct7
Distinct (%)7.4%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean3.9263158
Minimum0
Maximum8
Zeros12
Zeros (%)12.6%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:50.163625image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum0
5-th percentile0
Q13
median4
Q35
95-th percentile6
Maximum8
Range8
Interquartile range (IQR)2

Descriptive statistics

Standard deviation1.8232663
Coefficient of variation (CV)0.46437078
Kurtosis0.6562661
Mean3.9263158
Median Absolute Deviation (MAD)1
Skewness-0.89984563
Sum373
Variance3.3243001
MonotonicityNot monotonic
2022-12-05T03:47:50.219944image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=7)
ValueCountFrequency (%)
5 31
32.6%
4 28
29.5%
0 12
 
12.6%
3 10
 
10.5%
6 9
 
9.5%
2 3
 
3.2%
8 2
 
2.1%
ValueCountFrequency (%)
0 12
 
12.6%
2 3
 
3.2%
3 10
 
10.5%
4 28
29.5%
5 31
32.6%
6 9
 
9.5%
8 2
 
2.1%
ValueCountFrequency (%)
8 2
 
2.1%
6 9
 
9.5%
5 31
32.6%
4 28
29.5%
3 10
 
10.5%
2 3
 
3.2%
0 12
 
12.6%

date_added
Categorical

Distinct22
Distinct (%)23.2%
Missing0
Missing (%)0.0%
Memory size888.0 B
06-18-2019
32 
07/03/2019
10 
05/03/2019
10 
04/04/2019
06-25-2019
Other values (17)
29 

Length

Max length10
Median length10
Mean length10
Min length10

Characters and Unicode

Total characters950
Distinct characters12
Distinct categories3 ?
Distinct scripts1 ?
Distinct blocks1 ?
The Unicode Standard assigns character properties to each code point, which can be used to analyse textual variables.

Unique

Unique11 ?
Unique (%)11.6%

Sample

1st row07-17-2019
2nd row10/06/2018
3rd row07/03/2019
4th row06/02/2019
5th row07/03/2019

Common Values

ValueCountFrequency (%)
06-18-2019 32
33.7%
07/03/2019 10
 
10.5%
05/03/2019 10
 
10.5%
04/04/2019 7
 
7.4%
06-25-2019 7
 
7.4%
04/03/2019 6
 
6.3%
06/11/2019 3
 
3.2%
02/03/2019 3
 
3.2%
12/05/2018 2
 
2.1%
06/02/2019 2
 
2.1%
Other values (12) 13
13.7%

Length

2022-12-05T03:47:50.286721image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram of lengths of the category
ValueCountFrequency (%)
06-18-2019 32
33.7%
05/03/2019 10
 
10.5%
07/03/2019 10
 
10.5%
04/04/2019 7
 
7.4%
06-25-2019 7
 
7.4%
04/03/2019 6
 
6.3%
06/11/2019 3
 
3.2%
02/03/2019 3
 
3.2%
12/05/2018 2
 
2.1%
06/02/2019 2
 
2.1%
Other values (12) 13
13.7%

Most occurring characters

ValueCountFrequency (%)
0 236
24.8%
1 145
15.3%
2 112
11.8%
/ 102
10.7%
9 91
 
9.6%
- 88
 
9.3%
6 50
 
5.3%
8 37
 
3.9%
3 32
 
3.4%
4 22
 
2.3%
Other values (2) 35
 
3.7%

Most occurring categories

ValueCountFrequency (%)
Decimal Number 760
80.0%
Other Punctuation 102
 
10.7%
Dash Punctuation 88
 
9.3%

Most frequent character per category

Decimal Number
ValueCountFrequency (%)
0 236
31.1%
1 145
19.1%
2 112
14.7%
9 91
 
12.0%
6 50
 
6.6%
8 37
 
4.9%
3 32
 
4.2%
4 22
 
2.9%
5 21
 
2.8%
7 14
 
1.8%
Other Punctuation
ValueCountFrequency (%)
/ 102
100.0%
Dash Punctuation
ValueCountFrequency (%)
- 88
100.0%

Most occurring scripts

ValueCountFrequency (%)
Common 950
100.0%

Most frequent character per script

Common
ValueCountFrequency (%)
0 236
24.8%
1 145
15.3%
2 112
11.8%
/ 102
10.7%
9 91
 
9.6%
- 88
 
9.3%
6 50
 
5.3%
8 37
 
3.9%
3 32
 
3.4%
4 22
 
2.3%
Other values (2) 35
 
3.7%

Most occurring blocks

ValueCountFrequency (%)
ASCII 950
100.0%

Most frequent character per block

ASCII
ValueCountFrequency (%)
0 236
24.8%
1 145
15.3%
2 112
11.8%
/ 102
10.7%
9 91
 
9.6%
- 88
 
9.3%
6 50
 
5.3%
8 37
 
3.9%
3 32
 
3.4%
4 22
 
2.3%
Other values (2) 35
 
3.7%

year
Real number (ℝ)

Distinct2
Distinct (%)2.1%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean2018.9579
Minimum2018
Maximum2019
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:50.339544image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum2018
5-th percentile2019
Q12019
median2019
Q32019
95-th percentile2019
Maximum2019
Range1
Interquartile range (IQR)0

Descriptive statistics

Standard deviation0.20189472
Coefficient of variation (CV)9.9999471 × 10-5
Kurtosis19.887875
Mean2018.9579
Median Absolute Deviation (MAD)0
Skewness-4.6335232
Sum191801
Variance0.040761478
MonotonicityNot monotonic
2022-12-05T03:47:50.394361image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=2)
ValueCountFrequency (%)
2019 91
95.8%
2018 4
 
4.2%
ValueCountFrequency (%)
2018 4
 
4.2%
2019 91
95.8%
ValueCountFrequency (%)
2019 91
95.8%
2018 4
 
4.2%

month
Real number (ℝ)

Distinct10
Distinct (%)10.5%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean5.6736842
Minimum1
Maximum12
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:50.452168image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile2
Q15
median6
Q36
95-th percentile7
Maximum12
Range11
Interquartile range (IQR)1

Descriptive statistics

Standard deviation1.7891048
Coefficient of variation (CV)0.31533386
Kurtosis3.8052285
Mean5.6736842
Median Absolute Deviation (MAD)0
Skewness0.54053379
Sum539
Variance3.2008959
MonotonicityNot monotonic
2022-12-05T03:47:50.508978image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=10)
ValueCountFrequency (%)
6 48
50.5%
7 13
 
13.7%
4 13
 
13.7%
5 10
 
10.5%
2 4
 
4.2%
1 2
 
2.1%
12 2
 
2.1%
10 1
 
1.1%
3 1
 
1.1%
11 1
 
1.1%
ValueCountFrequency (%)
1 2
 
2.1%
2 4
 
4.2%
3 1
 
1.1%
4 13
 
13.7%
5 10
 
10.5%
6 48
50.5%
7 13
 
13.7%
10 1
 
1.1%
11 1
 
1.1%
12 2
 
2.1%
ValueCountFrequency (%)
12 2
 
2.1%
11 1
 
1.1%
10 1
 
1.1%
7 13
 
13.7%
6 48
50.5%
5 10
 
10.5%
4 13
 
13.7%
3 1
 
1.1%
2 4
 
4.2%
1 2
 
2.1%

day
Real number (ℝ)

Distinct14
Distinct (%)14.7%
Missing0
Missing (%)0.0%
Infinite0
Infinite (%)0.0%
Mean11.378947
Minimum1
Maximum30
Zeros0
Zeros (%)0.0%
Negative0
Negative (%)0.0%
Memory size888.0 B
2022-12-05T03:47:50.566784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Quantile statistics

Minimum1
5-th percentile3
Q13
median11
Q318
95-th percentile25
Maximum30
Range29
Interquartile range (IQR)15

Descriptive statistics

Standard deviation8.4352086
Coefficient of variation (CV)0.74129955
Kurtosis-1.315944
Mean11.378947
Median Absolute Deviation (MAD)7
Skewness0.34121383
Sum1081
Variance71.152744
MonotonicityNot monotonic
2022-12-05T03:47:50.621601image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Histogram with fixed size bins (bins=14)
ValueCountFrequency (%)
18 33
34.7%
3 29
30.5%
4 9
 
9.5%
25 7
 
7.4%
5 4
 
4.2%
11 3
 
3.2%
2 2
 
2.1%
30 2
 
2.1%
17 1
 
1.1%
6 1
 
1.1%
Other values (4) 4
 
4.2%
ValueCountFrequency (%)
1 1
 
1.1%
2 2
 
2.1%
3 29
30.5%
4 9
 
9.5%
5 4
 
4.2%
6 1
 
1.1%
10 1
 
1.1%
11 3
 
3.2%
12 1
 
1.1%
17 1
 
1.1%
ValueCountFrequency (%)
30 2
 
2.1%
26 1
 
1.1%
25 7
 
7.4%
18 33
34.7%
17 1
 
1.1%
12 1
 
1.1%
11 3
 
3.2%
10 1
 
1.1%
6 1
 
1.1%
5 4
 
4.2%

Interactions

2022-12-05T03:47:46.796934image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.526958image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.280055image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.003634image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.732208image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.535998image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.261572image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.004374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.827802image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.547412image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.293947image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.094273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.857730image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.585761image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.337860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.059447image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.791011image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.589819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.318382image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.061183image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.882621image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.604221image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.353748image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.147097image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.914542image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.642571image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.395667image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.117254image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.853697image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.646629image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.381172image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.120984image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.940425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.663546image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.410557image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.204903image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.976333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.701374image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.455467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.177054image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.915491image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.708422image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.443353image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.183774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.001222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.727333image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.472350image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.263708image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.042114image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.805028image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.516264image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.241784image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.975067image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.769219image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.506143image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.253540image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.064519image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.791122image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.534145image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.323506image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.101914image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.862836image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.576064image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.301584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.033076image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.825032image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.567939image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.314337image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.124319image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.850430image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.593943image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.381313image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.162712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.924628image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.636860image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.365712image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.100849image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.888819image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.631725image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.449885image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.185622image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.919200image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.659723image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.445100image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.227494image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:38.994395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.699651image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.433659image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.231019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.956592image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.697505image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.515846image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.252398image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.986973image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.801250image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.507397image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.287293image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.048731image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.754467image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.490469image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.290818image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.015395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.756309image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.573652image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.308211image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.046774image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.858060image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.561720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.351080image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.110522image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.823237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.553262image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.357595image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.077188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.823085image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.641425image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.371001image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.113551image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.919853image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.626503image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.408887image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.170420image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.881044image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.611068image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.417395image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.146956image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.884273image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.702222image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.429806image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.174346image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.979658image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.684311image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:47.468688image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.225237image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:39.942837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:40.672862image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:41.474206image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.201772image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:42.941584image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:43.763019image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:44.488609image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:45.234149image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.036466image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
2022-12-05T03:47:46.739127image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Correlations

2022-12-05T03:47:50.697349image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Auto

The auto setting is an interpretable pairwise column metric of the following mapping:
  • Variable_type-Variable_type : Method, Range
  • Categorical-Categorical : Cramer's V, [0,1]
  • Numerical-Categorical : Cramer's V, [0,1] (using a discretized numerical column)
  • Numerical-Numerical : Spearman's ρ, [-1,1]
The number of bins used in the discretization for the Numerical-Categorical column pair can be changed using config.correlations["auto"].n_bins. The number of bins affects the granularity of the association you wish to measure.

This configuration uses the recommended metric for each pair of columns.
2022-12-05T03:47:50.830902image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Spearman's ρ

The Spearman's rank correlation coefficient (ρ) is a measure of monotonic correlation between two variables, and is therefore better in catching nonlinear monotonic correlations than Pearson's r. It's value lies between -1 and +1, -1 indicating total negative monotonic correlation, 0 indicating no monotonic correlation and 1 indicating total positive monotonic correlation.

To calculate ρ for two variables X and Y, one divides the covariance of the rank variables of X and Y by the product of their standard deviations.
2022-12-05T03:47:50.944521image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Pearson's r

The Pearson's correlation coefficient (r) is a measure of linear correlation between two variables. It's value lies between -1 and +1, -1 indicating total negative linear correlation, 0 indicating no linear correlation and 1 indicating total positive linear correlation. Furthermore, r is invariant under separate changes in location and scale of the two variables, implying that for a linear function the angle to the x-axis does not affect r.

To calculate r for two variables X and Y, one divides the covariance of X and Y by the product of their standard deviations.
2022-12-05T03:47:51.055151image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Kendall's τ

Similarly to Spearman's rank correlation coefficient, the Kendall rank correlation coefficient (τ) measures ordinal association between two variables. It's value lies between -1 and +1, -1 indicating total negative correlation, 0 indicating no correlation and 1 indicating total positive correlation.

To calculate τ for two variables X and Y, one determines the number of concordant and discordant pairs of observations. τ is given by the number of concordant pairs minus the discordant pairs divided by the total number of pairs.
2022-12-05T03:47:51.162720image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Cramér's V (φc)

Cramér's V is an association measure for nominal random variables. The coefficient ranges from 0 to 1, with 0 indicating independence and 1 indicating perfect association. The empirical estimators used for Cramér's V have been proved to be biased, even for large samples. We use a bias-corrected measure that has been proposed by Bergsma in 2013 that can be found here.
2022-12-05T03:47:51.259840image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/

Phik (φk)

Phik (φk) is a new and practical correlation coefficient that works consistently between categorical, ordinal and interval variables, captures non-linear dependency and reverts to the Pearson correlation coefficient in case of a bivariate normal input distribution. There is extensive documentation available here.

Missing values

2022-12-05T03:47:47.574837image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
A simple visualization of nullity by column.
2022-12-05T03:47:47.770188image/svg+xmlMatplotlib v3.6.2, https://matplotlib.org/
Nullity matrix is a data-dense display which lets you quickly visually pick out patterns in data completion.

Sample

property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthday
03477958https://www.zameen.com/Property/lahore_model_town_6_kanal_excellent_house_for_sale_in_model_town-347795-8-1.htmlHouse220000000Very HighModel TownLahorePunjabModel Town, Lahore, Punjab31.48386974.32568606 Kanal120.032670.12For Sale007-17-20192019717
148289248https://www.zameen.com/Property/lahore_multan_road_1_kanal_house_for_sale-482892-48-1.htmlHouse40000000Very HighMultan RoadLahorePunjabMultan Road, Lahore, Punjab31.43159374.17998051 Kanal20.05445.02For Sale510/06/20182018106
255596275https://www.zameen.com/Property/eden_eden_avenue_9_marla_house_for_sale-555962-75-1.htmlHouse9500000LowEdenLahorePunjabEden, Lahore, Punjab31.49934874.41695909 Marla9.02450.26For Sale307/03/2019201973
37852893102https://www.zameen.com/Property/gulberg_paf_falcon_complex_matz_service_offer_1_kanal_house_for_sale-785289-3102-1.htmlHouse52000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.49590974.35056961 Kanal20.05445.02For Sale506/02/2019201962
49830653749https://www.zameen.com/Property/eme_society_eme_society_block_e_house_for_sale-983065-3749-1.htmlHouse32500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43997874.20968501 Kanal20.05445.02For Sale507/03/2019201973
59830663745https://www.zameen.com/Property/eme_society_eme_society_block_a_house_for_sale-983066-3745-1.htmlHouse31500000HighEME SocietyLahorePunjabEME Society, Lahore, Punjab31.43774474.21349001 Kanal20.05445.02For Sale607/03/2019201973
612866433733https://www.zameen.com/Property/eden_eden_palace_villas_7_5_marla_luxury_house_is_available_for_sale-1286643-3733-1.htmlHouse13500000MediumEdenLahorePunjabEden, Lahore, Punjab31.44111374.23968347.5 Marla7.52041.88For Sale404/04/2019201944
71402784514https://www.zameen.com/Property/lahore_upper_mall_commercial_old_house_for_sale_upper_mall_lahore_excellent_location-1402784-514-1.htmlHouse87500000Very HighUpper MallLahorePunjabUpper Mall, Lahore, Punjab31.54211474.35589851.2 Kanal24.06534.02For Sale406-30-20192019630
8160671069https://www.zameen.com/Property/lahore_cavalry_ground_10_marla_house_is_available_for_sale-1606710-69-1.htmlHouse16500000HighCavalry GroundLahorePunjabCavalry Ground, Lahore, Punjab31.50055774.367730010 Marla10.02722.51For Sale404/04/2019201944
916468801781https://www.zameen.com/Property/bahria_town_sector_b_bahria_town_umar_block_double_storey_house_for_sale-1646880-1781-1.htmlHouse18500000HighBahria TownLahorePunjabBahria Town, Lahore, Punjab31.38170674.195294010 Marla10.02722.51For Sale007/04/2019201974
property_idlocation_idpage_urlproperty_typepriceprice_binlocationcityprovince_namelocalitylatitudelongitudebathsareaarea_marlaarea_sqftpurposebedroomsdate_addedyearmonthday
8545368543852https://www.zameen.com/Property/gulberg_main_boulevard_gulberg_commercialized_piece_of_property_house_for_sale-4536854-3852-1.htmlHouse1200000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51943574.34554808 Kanal160.043560.16For Sale004/04/2019201944
8645577993850https://www.zameen.com/Property/gulberg_mm_alam_road_shopping_paradise_of_lahore_house_for_sale-4557799-3850-1.htmlHouse380000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.51047174.35052602 Kanal40.010890.04For Sale005/03/2019201953
874560911496https://www.zameen.com/Property/mozang_mozang_chungi_al_qader_center_ground_floor_new_apartment_for_sale-4560911-496-1.htmlFlat3200000LowMozangLahorePunjabMozang, Lahore, Punjab31.54913574.31511723 Marla3.0816.75For Sale207/03/2019201973
8845709881447https://www.zameen.com/Property/dha_defence_dha_phase_5_original_faisal_rasool_brand_new_classical_bungalow-4570988-1447-1.htmlHouse59800000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46249374.40934271 Kanal20.05445.02For Sale605/03/2019201953
8946771018172https://www.zameen.com/Property/dha_defence_defence_raya_dha_raya_2_kanal_facing_golf_course_fully_basement_house_for_sale-4677101-8172-1.htmlHouse66000000Very HighDHA DefenceLahorePunjabDHA Defence, Lahore, Punjab31.46915574.47055362 Kanal40.010890.04For Sale606-25-20192019625
904747413154https://www.zameen.com/Property/gulberg_zafar_ali_road_6_kanal_house_for_sale_on_zafar_ali_road_mall_road_upper_mall_lahore_excellent_location-4747413-154-1.htmlHouse360000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.53842074.352357710 Kanal200.054450.20For Sale506-30-20192019630
914907568514https://www.zameen.com/Property/lahore_upper_mall_4_kanal__house_for_sale_upper_mall_lahore-4907568-514-1.htmlHouse160000000Very HighUpper MallLahorePunjabUpper Mall, Lahore, Punjab31.54211474.35589861 Kanal20.05445.02For Sale501-18-20192019118
9249406298https://www.zameen.com/Property/lahore_model_town_3_marla_house_for_sale-4940629-8-1.htmlHouse8000000LowModel TownLahorePunjabModel Town, Lahore, Punjab31.47388474.32908033 Marla3.0816.75For Sale207/03/2019201973
9349514517https://www.zameen.com/Property/lahore_gulberg_blue_zone_house_for_sale-4951451-7-1.htmlHouse480000000Very HighGulbergLahorePunjabGulberg, Lahore, Punjab31.52236174.34717204 Kanal80.021780.08For Sale005/03/2019201953
9450193101534https://www.zameen.com/Property/garden_town_garden_town_ahmed_block_purely_residential_apartments_for_sale-5019310-1534-1.htmlFlat22500000Very HighGarden TownLahorePunjabGarden Town, Lahore, Punjab31.50780074.31873307 Marla7.01905.76For Sale004/03/2019201943